Methods of and devices for reducing structure noise through self-structure analysis

ABSTRACT

To reduce structure noise, input data representing an input structure is obtained and boundary conditions are set by classifying data of each of multiple structure elements of the input data as a signal component or a noise component. A smoothing operation is performed with respect to the input data and based on the boundary conditions. Output data representing an output structure is provided by reducing noise from the input structure.

CROSS-REFERENCE TO RELATED APPLICATION

This U.S. non-provisional patent application claims priority under 35USC § 119 to Korean Patent Application No. 10-2016-0165559, filed onDec. 7, 2016 in the Korean Intellectual Property Office (KIPO), thedisclosure of which is incorporated by reference in its entirety herein.

BACKGROUND 1. Technical Field

Example embodiments of the present disclosure relate generally to noisereduction. More particularly, example embodiments of the presentdisclosure relate to methods of and devices for reducing structure noisethrough self-structure analysis.

2. Discussion of the Related Art

Data representing a two-dimensional or three-dimensional structure mayinclude noise. It is not easy to separate and remove the structure noiseor errors from the original structure data. The removal of structurenoise may significantly increase processing time for processing thestructure data.

SUMMARY

Some example embodiments of the present disclosure may provide a methodand a device for efficiently reducing structure noise throughself-structure analysis. The self-structure analysis may be provided by,for example, analyzing (evaluating, processing etc.) input data of astructure to identify and remove noise in the input data of thestructure. The self-structure analysis may be performed withoutrequiring additional input beyond the input data.

Some example embodiments of the present disclosure may provide anelectronic system of executing program routines of efficiently reducingstructure noise through self-structure analysis.

According to example embodiments of the present disclosure, a method ofreducing structure noise includes obtaining input data representing aninput structure. The method also includes setting boundary conditions byclassifying data of each of multiple structure elements of the inputdata as a signal component or a noise component. A smoothing operationis performed with respect to the input data based on the boundaryconditions. Output data representing an output structure is provided byreducing noise from the input structure.

According to example embodiments, a device for reducing structure noiseincludes a controller configured to set boundary conditions byclassifying data of each of multiple structure elements of the inputdata as a signal component or a noise component based on input datarepresenting an input structure. A smoothing element (e.g., a processor,a circuit or other component(s)) of the device is configured to performa smoothing operation with respect to the input data based on theboundary conditions and to provide output data representing an outputstructure by reducing noise from the input structure.

According to example embodiments of the present disclosure, acomputer-based electronic system for reducing structure noise includesan input device, a memory device, an output device and a processor. Theinput device is configured to receive input data representing an inputstructure. The memory device is configured to store informationincluding program routines. When executed, the program routines setboundary conditions by classifying data of each of multiple structureelements of the input data as a signal component or a noise component,perform a smoothing operation with respect to the input data based onthe boundary conditions. and provide output data representing an outputstructure by reducing noise from the input structure based on theclassifying of the noise components. The output device is configured todisplay the input structure and the output structure. The processor isconnected to the input device, the output device and the memory device.The processor is configured to control an execution of the programroutines.

The method and device according to example embodiments of the presentdisclosure may reduce structure noise efficiently with respect to anarbitrary input structure without requiring additional information onthe signal components of the input structure by analyzing the data ofthe input structure itself to set the boundary condition and byseparating the noise from the data of the valid structure. In addition,in comparison with conventional schemes of reducing noise by addingstructure elements, the method and device according to exampleembodiments of the present disclosure may reduce the structure noisewith a decreased data processing time by sequentially removing data ofstructure elements to simultaneously remove the errors in the data ofthe input structure.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments of the present disclosure will be more clearlyunderstood from the following detailed description taken in conjunctionwith the accompanying drawings.

FIG. 1 is a flow chart illustrating a method of reducing structure noiseaccording to example embodiments of the present disclosure.

FIG. 2 is a diagram for describing structure noise removal by settingboundary conditions.

FIG. 3 is a block diagram illustrating a device for reducing structurenoise according to example embodiments of the present disclosure.

FIG. 4 is a flow chart illustrating an example embodiment of settingboundary conditions included in the method of FIG. 1.

FIG. 5 is a diagram illustrating an example of face groups and arearatios for setting boundary conditions of FIG. 4.

FIG. 6 is a diagram illustrating an example embodiment of determining athreshold ratio for setting boundary conditions of FIG. 4.

FIGS. 7A, 7B and 7C are diagrams illustrating an example result of anoise reduction according to the method of FIG. 1.

FIG. 8 is a flow chart illustrating an example embodiment of settingboundary conditions based on a result of comparing an area ratio and athreshold ratio.

FIG. 9 is a diagram for describing a process of setting boundaryconditions of FIG. 8.

FIG. 10 is a flow chart illustrating a method of reducing structurenoise according to example embodiments of the present disclosure.

FIG. 11 is a flow chart illustrating a method of reducing structurenoise according to example embodiments of the present disclosure.

FIG. 12 is a flow chart illustrating an example embodiment of performinga smoothing operation and removing data of a portion of structureelements included in the method of FIG. 11.

FIGS. 13A, 13B and 13C are diagrams for describing an example embodimentof removing a minimum edge in FIG. 12.

FIGS. 14A and 14B are diagrams illustrating an example result of a noisereduction according to the method of FIG. 11.

FIG. 15 is a flow chart illustrating a method of reducing structurenoise according to example embodiments of the present disclosure.

FIG. 16 is a flow chart illustrating an example embodiment of performinga smoothing operation and canceling a portion of boundary conditionsincluded in the method of FIG. 15.

FIG. 17 is a diagram for describing an example embodiment of canceling aboundary condition in FIG. 16.

FIGS. 18A, 18B and 14C are diagrams illustrating an example result of anoise reduction according to the method of FIG. 15.

FIG. 19 is a flow chart illustrating an example embodiment of performinga smoothing operation, removing data of a portion of structure elementsand canceling a portion of boundary conditions included in a method ofreducing structure noise according to example embodiments of the presentdisclosure.

FIGS. 20A, 20B and 20C are diagrams for describing an example embodimentof a smoothing operation included in a method of reducing structurenoise according to example embodiments of the present disclosure.

FIG. 21 is a flow chart illustrating a method of reducing structurenoise according to example embodiments of the present disclosure.

FIG. 22 is a flow chart illustrating an example embodiment of combiningstructure elements included in the method of FIG. 21.

FIGS. 23A and 23B are diagrams for describing an example embodiment ofcombining structure elements of FIG. 22.

FIGS. 24A and 24B are diagrams illustrating an example result of a noisereduction according to the method of FIG. 21.

FIG. 25 is a block diagram illustrating an electronic system accordingto example embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Various example embodiments of the present disclosure will be describedmore fully hereinafter with reference to the accompanying drawings, inwhich some example embodiments are shown. In the drawings, like numeralsrefer to like elements throughout. The repeated descriptions may beomitted.

FIG. 1 is a flow chart illustrating a method of reducing structure noiseaccording to example embodiments of the present disclosure.

Referring to FIG. 1, input data representing an input structure isprovided (S200). The input data may be obtained, received, collected, orinput. The input data may be received over a link, or a network, or maybe retrieved from a computer readable medium. As used herein, the inputdata may be referred to interchangeably as the input structure, sincethe input data represents the input structure. The input structure maybe a two-dimensional structure or a three-dimensional structure and theinput structure may be provided through various methods. In some exampleembodiments, the input data may be generated by an electronic designautomation (EDA) tool. In other example embodiments, the input data maybe provided by processing image data that is captured using an imagesensor. In still other example embodiments, the input data may beprovided by restructuring data using a computer vision. The method ofproviding the input data is not limited thereto and the input data maybe provided through other various methods.

Boundary conditions are set by classifying data of each of multiplestructure elements of the input data as a signal component or a noisecomponent based on the input data (S400). The two-dimensional or threedimensional input structure may be a figure having a shape that isdivided by polyhedrons. The two-dimensional input structure may becomposed of structure elements of vertexes, edges and faces. Thethree-dimensional input structure may be composed of structure elementsof vertexes, edges, faces and solids. The input data of the structuremay include signal components reflecting correct structure informationand noise components corresponding to errors caused during processes ofgenerating the representation of the structure.

Due to the noise components, the number of the structure elements may beincreased significantly. Thus, a calculation amount may be increased.The noise components may have a complex local shape. Additionally, thenoise components may cause or reflect the existence of structural errorssuch as a cross of faces. In these cases, errors may be caused duringprocesses of removing noise and a probability of structure distortionmay be increased.

This disclosure provides example embodiments of methods of and devicesfor removing or reducing the structure noise that are caused in the dataof the representation of the two-dimensional or three-dimensionalstructure. As used herein, the removal may be described as removal of astructural element such as an edge or as removal of data element of astructural element such an edge. It should be understood that datadescribed herein is representative of a two-dimensional orthree-dimensional structure, such that removal of a structural elementcorresponds to removal of data of the structural element. Thus, removalof a structural element may be described interchangeably with removal ofdata of a structural element. To reduce loss of structure in therepresentation during the noise removal process, the input data itselfis analyzed without additional information on the input structure toclassify data of each of the structure elements of the input data as thesignal component or the noise component. Example embodiments of settingthe boundary conditions based on the input data will be described belowwith reference to FIGS. 4 through 9.

A smoothing operation is performed with respect to the input data basedon the boundary conditions (S600). Output data representing an outputstructure is provided by reducing noise from the input structure (S800).

The smoothing operation may be performed variously. For example, thesmoothing operation may be performed by a restructuring method using asurface energy minimization. The structure calculation based on thesurface energy minimization is used mainly in a field of materialscience, which is applied to a grain growth of polycrystalline material,a solder shape of packaging material, a fluid shape prediction, etc. Theexample embodiments of the smoothing operation will be described belowwith reference to FIGS. 20A, 20B and 20C.

In conventional schemes, structure noise is removed by additionallydividing the structure elements to apply an average of coordinates ofthe adjacent vertexes, or by applying a function to vertex coordinatesduring structure modification to prevent noise generation. However, inthese schemes, the noise component may be maintained and the signalcomponent may be distorted because the structure modification isperformed without differentiating the signal component and the noisecomponent.

Particularly, if the structure modification is applied repeatedly to aseverely projected noise component, the vulnerable signal component nearthe severely projected noise component may be distorted before theremoval of the noise component. The calculation error between thedistorted structure elements may increase the structure noise.Additionally, the processing time may be increased by the increasedstructure elements used in interpolation of structure. The scheme ofremoving noise using a structure function requires that the signalcomponents should be defined as a function in advance. Thus, the schememay be applied to a structure of limited shapes.

The smoothing operation based on the surface energy minimization iseffective in removing a projected noise but it does not differentiatethe signal component and the noise component. Thus, the above-mentionedproblems such as the structure distortion, the increase of theprocessing time, etc. may be caused as the smoothing operation isrepeated.

The method according to example embodiments of the present disclosuremay reduce structure noise efficiently with respect to an arbitraryinput structure without requiring additional information on the signalcomponents of the input structure. The structure noises may be reducedby analyzing the input structure itself to set the boundary conditionand by separating the noise from the data of the valid structure.

The structure with the noise reduced or removed can be reproduced fordisplay as a model. The model may be electronically reproduced in animage or video, and shown to a viewer as a visualization of theunderlying structure. As will be explained herein, this may provide anaccurate visualization of extraordinarily complex structures that wouldpotentially not be comprehensible to a user viewing the representationof the structure configured without excluding the noise.

FIG. 2 is a diagram for describing structure noise removal by settingboundary conditions.

In FIG. 2, a first structure STR11 represents an example input structureand a second structure STR12 is the same as the first structure STR11but boundary conditions (B.C.) are set to the second structure STR12. Athird structure STR13 represents an output structure corresponding to aresult of performing the smoothing operation with respect to the firststructure STR11 without the boundary conditions and a fourth structureSTR14 represents an output structure corresponding to a result ofperforming the smoothing operation with respect to the second structureSTR12 with the boundary conditions.

As represented by the third structure STR13, if the boundary conditionsare not set, the first structure STR11 may be distorted to a sphere bythe smoothing operation. In contrast, as represented by the fourthstructure STR14, if the boundary conditions are set, the original shapeof the second shape STR2 is maintained.

FIG. 3 is a block diagram illustrating a device for reducing structurenoise according to example embodiments of the present disclosure.

Referring to FIG. 3, a device 20 for reducing structure noise mayinclude a control unit CTRL 22 and a smoothing unit SMTH 24. Asdescribed herein, the control unit 22 and the smoothing unit 24 may beindividual processors, circuits or other forms of hardware capable ofexecuting software. Alternatively, the control unit 22 and the smoothingunit 24 may be a combination of one or more elements of hardware such asprocessors and corresponding units of software executed by the elementsof hardware.

The control unit 22 may set boundary conditions BC by classifying dataof each of multiple structure elements of the input data as a signalcomponent or a noise component based on input data Din representing aninput structure. As described above, the two-dimensional or threedimensional input structure represented by the input data Din may be afigure having a shape that is divided by polyhedrons. Thetwo-dimensional input structure may be composed of structure elements ofvertexes, edges and faces. The three-dimensional input structure may becomposed of structure elements of vertexes, edges, faces and solids.Example embodiments of setting the boundary conditions BC based on theinput data Din will be described below with reference to FIGS. 4 through9.

The smoothing unit 24 may perform a smoothing operation with respect tothe input data Din based on the boundary conditions BC, and provideoutput data Dout representing an output structure by reducing noise fromthe input structure Din. The smoothing unit 24 may be implementedvariously. For example, the smoothing unit 24 may perform the smoothingoperation by a restructuring method using a surface energy minimization.The example embodiments of the smoothing operation performed by thesmoothing unit 24 will be described below with reference to FIGS. 20A,20B and 20C.

As will be appreciated by one skilled in the art, embodiments of thedevice 20 and the method of reducing the structure noise may beimplemented with hardware, software or a combination of hardware andsoftware. For example, the device 20 may be implemented using a computerprogram product embodied in one or more computer readable medium(s)having computer readable program code embodied thereon. The computerreadable program code may be provided to a processor of a generalpurpose computer, special purpose computer, or other programmable dataprocessing apparatus. The computer readable medium may be a computerreadable signal medium or a computer readable storage medium. Thecomputer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

FIG. 4 is a flow chart illustrating an example embodiment of settingboundary conditions included in the method of FIG. 1. FIG. 5 is adiagram illustrating an example of face groups and area ratios forsetting boundary conditions of FIG. 4. FIG. 6 is a diagram illustratingan example embodiment of determining a threshold ratio for settingboundary conditions of FIG. 4.

Referring to FIGS. 3 and 4, the control unit 22 of the device 20 mayanalyze plane equations of faces included in the structure elementsbased on the input data Din (S410). The plane equation may berepresented as Equation1.ax+by+cz+d=0 or (a/d)x+(b/d)y+(c/d)z+1=0  Equation1

In Equation1, x, y and z indicate coordinates of a vertex included in ordefining a corresponding plane and a, b, c and d indicate coefficientsof the plane equation. By Equation1, three vertexes may define oneplane.

The control unit 22 may group the faces into face groups based on theplane equations of the faces (S420). The face groups may be representedby a combination of the coefficients of the plane equation, that is, [a,b, c, d] or [a/d, b/d, c/d, 1]

The control unit 22 may calculate area ratios such that each area ratiocorresponds to a ratio of a total area of each face group with respectto an entire area of the faces (S430). The area ratio of each face groupmay be represented by Equation2.ARi=AGi/AE  Equation2

In Equation2, ARi indicates the area ratio of the i-th face group, AGiindicates the total area of the faces included in the i-th face groupand AE indicates the entire area of all faces included in the inputstructure.

FIG. 5 illustrates an example of face groups and area ratios. Theexample of the face groups and the area ratios in FIG. 5 corresponds toan example input structure STR21 in FIG. 7A. In FIG. 5, a vertical axisrepresents the face groups as [a/d, b/d, c/d, 1] and a horizontal axisrepresents the area ratio in percentage % of the corresponding facegroup. The input structure STR21 in FIG. 7A has a rectangular shape.Thus, as illustrated in FIG. 5, the six area ratios corresponding to thesix faces of the rectangle may be greater than the area ratios of theother face groups.

Referring back to FIGS. 3 and 4, the control unit 22 may determine athreshold ratio ARth of the area ratios. In some example embodiments,the control unit 22 may determine the threshold ratio based on adistribution of numbers of the face groups having the respective arearatios.

FIG. 6 illustrates an example embodiment of determining a thresholdratio based on the distribution of the numbers of the face groups havingthe respective area ratios. As illustrated in FIG. 6, significantly moreof the face groups have relatively small area ratios between 0% through1% than the relatively great area ratios between 11% through 17%. Thatis, there are more face groups having area ratios below 1% than thereare having area ratios between 11% and 17%.

In some example embodiments, a range RG of the area ratios may beobtained such that the numbers of the face groups in the range RG arezero continuously and a center value of the range RG of the area ratiosmay be determined as the threshold ratio ARth. In case of FIG. 6, therange RG includes the area ratios between 1% through 11%, and the centervalue of 6% of the range RG may be determined as the threshold ratioARth.

In other example embodiments, the threshold ratio Arth may be acceptedfrom a user who determined the threshold ratio based on the distributionof numbers of the face groups having the respective area ratios. Asystem as will be described below with reference to FIG. 25 may includeinput-output devices such as a display, a keyboard, a mouse, a touchscreen, etc. The device 20 of FIG. 3 may show the distribution of thenumbers of the face groups to the user through the output devices suchas a display, and the user may determine a proper value of the thresholdratio ARth referring to the input structure of FIG. 7A.

Referring back to FIGS. 3 and 4, the control unit 22 may set theboundary conditions BC based on comparison results of the area ratiosand the threshold ratio ARth (S450). Setting of the boundary conditionsBC will be described below with reference to FIGS. 8 and 9.

FIGS. 7A, 7B and 7C are diagrams illustrating an example result of anoise reduction according to the method of FIG. 1.

FIG. 7A illustrates an example input structure STR21. FIG. 7Billustrates an input structure STR22 to which boundary conditions areset. FIG. 7C illustrates an output structure STR23 corresponding to aresult of performing the smoothing operation with respect to the inputstructure STR22 with the boundary conditions.

The boundary conditions set based on the comparison result of the arearatios of the face groups and the threshold ratio as described withreference to FIGS. 5 and 6 are represented in FIG. 7B. In FIG. 7B, theshaded triangular meshes correspond to the signal components and thenot-shaded triangular meshes correspond to the noise components. Thetriangular meshes correspond to the above-mentioned faces and eachtriangular mesh includes three edges and three vertexes.

In the conventional methods based on the surface energy minimization,the user has to assign a function (e.g., z=sin(x)), a coordinate (e.g.,y=0) and/or a condition (e.g., if distance(body1, body2)<0.1) as theboundary conditions giving restriction to the structure modification.However, the boundary conditions may be determined by analyzing theinput data itself according to example embodiments. For example, theplane equations of the faces are analyzed to group the faces and theareas of the faces in each face group are summed to obtain each arearatio. The faces in each face group may be classified as the signalcomponents if the area ratio is greater than the threshold ratio ARth,and may be classified as the noise components if the area ratio issmaller than the threshold ratio ARth. The boundary conditions or theconstraints may be set such than the structure elements of the faceclassified as the signal component may move only on the face while thesmoothing operation is performed. In contrast, the structure elements ofthe face classified as the noise component may move freely to beeffected by the structure modification for the surface energyminimization.

FIG. 8 is a flow chart illustrating an example embodiment of settingboundary conditions based on a result of comparing an area ratio and athreshold ratio. FIG. 9 is a diagram for describing a process of settingboundary conditions of FIG. 8.

Referring to FIGS. 3 and 8, the control unit 22 may set the boundaryconditions based on the comparison result of Equation3.ARi>ARth  Equation3

The control unit 22 may classify the faces as the signal components ifthe face group has the area ratio ARi greater than the threshold ratioARth (S451). In contrast, the control unit 22 may classify the faces asthe noise components if the face group has the area ratio ARi equal toor smaller than the threshold ratio ARth (S452).

FIG. 9 illustrates an example of the signal components and the noisecomponents that are classified as described above. In FIG. 9, the shadedtriangular meshes correspond to the signal components and the not-shadedtriangular meshes correspond to the noise components.

Referring to FIGS. 8 and 9, the control unit 22 may classify a vertex asthe signal component if the vertex pertains to at least one of the facesthat are classified as the signal component (S453). In contrast, thecontrol unit 22 may classify a vertex as the noise component if thevertex pertains to only the faces that are classified as the noisecomponent (S454). In the example of FIG. 9, only one vertex Pns pertainsto only the faces that are classified as the noise component and theother vertexes P1 through P16 pertain to at least one of the faces thatare classified as the signal component. Thus the vertex Pns may beclassified as the noise component whereas the other vertexes P1 throughP16 may be classified as the signal components.

After all of the vertexes are classified as the signal component or thenoise component, the control unit 22 may set the boundary conditionswith respect to the vertexes that are classified as the signalcomponent. For example, the boundary conditions or the constraints maybe set such than the vertex classified as the signal component may moveon the face while the smoothing operation such as the surface energyminimization is performed. In contrast, the structure elements of theface classified as the noise component may move freely to be effected bythe structure modification for the surface energy minimization.

FIG. 10 is a flow chart illustrating a method of reducing structurenoise according to example embodiments of the present disclosure.

Referring to FIG. 10, input data representing an input structure isprovided (S200). Boundary conditions are set by classifying data of eachof multiple structure elements of the input data as a signal componentor a noise component (S400). In addition to setting the boundaryconditions based on the input data, the boundary conditions may beaccepted as settings from a user (S420). A smoothing operation isperformed with respect to the input data based on the boundaryconditions (S600). Output data representing an output structure isprovided by reducing noise from the input structure (S800).

The processes S200, S400, S600 and S800 of the method of FIG. 10 are thesame as those of FIG. 1. Thus, the repeated descriptions are omitted.

This disclosure is for providing example embodiments of methods of anddevices for removing or reducing the structure noise that are caused inthe data of the two-dimensional or three-dimensional structure. Toreduce loss of structure during the noise removal process, the inputdata itself is analyzed without additional information on the inputstructure to classify data of each of the structure elements of theinput data as the signal component or the noise component. As describedwith reference to FIGS. 4 through 9, the boundary conditions may be setbased on the input data.

In addition to setting of the boundary conditions automatically based onthe input data, the boundary conditions may be determined by a userbased on the user's judgement or based on additional information on theinput structure. The boundary conditions based on the input data and theuser's boundary conditions may compensate for each other to contributeto the efficient execution of the structure noise reduction.

FIG. 11 is a flow chart illustrating a method of reducing structurenoise according to example embodiments of the present disclosure.

Referring to FIG. 11, input data representing an input structure isprovided (S200). The input structure may be a two-dimensional structureor a three-dimensional structure and the input structure may be providedthrough various methods. In some example embodiments, the input data maybe generated by an electronic design automation (EDA) tool. In otherexample embodiments, the input data may be provided by processing imagedata that is captured using an image sensor. In still other exampleembodiments, the input data may be provided by restructuring data usinga computer vision. The method of providing the input data is not limitedthereto and the input data may be provided through other variousmethods.

Boundary conditions are set (S440). In some example embodiments, theboundary conditions may be set by classifying data of each of multiplestructure elements of the input data as a signal component or a noisecomponent. In other example embodiments, the boundary conditions may beaccepted from a user. In still other example embodiments, the boundaryconditions may be set by classifying the structure elements with thesettings accepted as input from the user.

The two-dimensional or three dimensional input structure may be a figurehaving a shape that is divided by polyhedrons. The two-dimensional inputstructure may be composed of structure elements of vertexes, edges andfaces. The three-dimensional input structure may be composed ofstructure elements of vertexes, edges, faces and solids. The inputstructure may include signal components reflecting correct structureinformation and noise components corresponding to errors caused duringprocesses of generating the structure.

A smoothing operation is performed with respect to the input data basedon the boundary conditions (S600). Data of a portion of the structureelements is removed while the smoothing operation is performed (S720).After the smoothing operation accompanying the removal of the data ofthe structure elements, output data representing an output structure isprovided by reducing noise from the input structure (S800). Thesmoothing operation accompanying the removal of the data of thestructure elements will be described below with reference to FIGS. 12through 14B.

In conventional schemes, the structure noise has been removed byadditionally dividing the structure elements to apply an average ofcoordinates of the adjacent vertexes, or by applying a function tovertex coordinates during structure modification to prevent noisegeneration. However, in these schemes, the noise component may bemaintained and the signal component may be distorted because thestructure modification is performed without differentiating the signalcomponent and the noise component.

Particularly if the structure modification is applied repeatedly to aseverely projected noise component, the vulnerable signal component nearthe severely projected noise component may be distorted before theremoval of the noise component. The calculation error between thedistorted structure elements may increase the structure noise.Additionally, the processing time may be increased by the increasedstructure elements used in interpolation of structure. The scheme ofremoving noise using a structure function requires that the signalcomponents should be defined as a function in advance. Thus, the schememay be applied to a structure of limited shapes.

The smoothing operation based on the surface energy minimization iseffective in removing a projected noise but it does not differentiatethe signal component and the noise component. Thus, the above-mentionedproblems such as the structure distortion, the increase of theprocessing time, etc. may be caused as the smoothing operation isrepeated.

The method according to example embodiments of the present disclosuremay reduce structure noise efficiently with respect to an arbitraryinput structure without requiring additional information on the signalcomponents of the input structure. Structure noises are reduced in thisway by analyzing the input structure itself to set the boundarycondition, and by separating the noise from the data of the validstructure.

Due to the noise components, the number of the structure elements may beincreased significantly. Thus, a calculation amount may be increased.The noise components may have a complex local shape. Additionally, thenoise components may cause or reflect the existence of structural errorssuch as a cross of faces. In these cases, errors may be caused duringprocesses of removing noise, and a probability of structure distortionmay be increased.

The noise component may transfer wrong information of the distortedstructure and also affect the time of the operation (e.g., a Booleanoperation) for removing the structure noise. As the number of thestructure elements is increased, the operation time or the dataprocessing time is increased and a probability of reducing the noise isdecreased. For the reliable data processing, the signal componentsshould be maintained and the noise components should be removed as muchas possible. In addition, the number of the structure elements shouldnot be increased to enhance efficiency of the noise reduction, becausethe data processing time is increased and the probability of reducingthe noise is decreased as the number of the structure elements isincreased.

The method and device according to example embodiments may reducestructure noise efficiently with respect to an arbitrary input structurewithout requiring additional information on the signal components of theinput structure by analyzing the input structure itself to set theboundary condition and by separating the noise from the data of thevalid structure.

In addition, in comparison with conventional schemes of reducing noiseby adding structure elements, the method and device according to exampleembodiments may reduce the structure noise with a decreased dataprocessing time by sequentially removing data of structure elements tosimultaneously remove the errors in the input structure.

FIG. 12 is a flow chart illustrating an example embodiment of performinga smoothing operation and removing data of a portion of structureelements included in the method of FIG. 11.

Referring to FIGS. 3 and 12, the smoothing unit 24 performs a smoothingsub-routine (S610). The above mentioned smoothing operation may beperformed such that the smoothing sub-routine is repeatedly executedbased on input data representing a structure and boundary conditions.

The smoothing operation may be performed variously. For example, thesmoothing operation may be performed by a restructuring method using asurface energy minimization. The structure calculation based on thesurface energy minimization is used mainly in a field of materialscience, which is applied to a grain growth of polycrystalline material,a solder shape of packaging material, a fluid shape prediction, etc. Theexample embodiments of the smoothing operation will be described belowwith reference to FIGS. 20A, 20B and 20C.

The control unit 22 may determine whether smoothing is completed (S620)whenever the smoothing sub-routine of S610 is finished. The completionof smoothing may be determined variously, for example, depending on thescheme of the smoothing process, the degree of the noise reduction, etc.

In some example embodiments, when the smoothing process is performedbased on the surface energy minimization, the surface energy before thesmoothing sub-routine and the surface energy after the smoothingsub-routine may be obtained. It may be determined that smoothing iscompleted if the amount of reduction of the surface energy from beforeto after the smoothing sub-routine is smaller than a reference value. Inother example embodiments, a displacement of a vertex classified as anoise component may be obtained, and it is determined that smoothing iscompleted if the displacement is smaller than a reference value.

When the control unit 22 determines that smoothing is completed (S620:YES), the smoothing operation is completed, and the smoothing unit 24may provide the output data Dout representing the output structure byreducing noise from the input structure.

When the control unit 22 determines that smoothing is not completed(S620: NO), the control unit 22 removes a minimum edge among theexisting entire edges (S722). The smoothing unit 24 performs thesmoothing sub-routine again (S610) based on the input data and theboundary conditions excluding the removed minimum edge. The removal ofthe minimum edge will be described below with reference to FIGS. 13A,13B and 13C.

FIGS. 13A, 13B and 13C are diagrams for describing an example embodimentof removing a minimum edge in FIG. 12.

Referring to FIG. 13A, as described above, only one vertex Pns pertainsto only the faces that are classified as the noise component. The othervertexes P1 through P16 pertain to at least one of the faces that areclassified as the signal component. Thus the vertex Pns may beclassified as the noise component whereas the other vertexes P1 throughP16 may be classified as the signal components.

For example, an edge connecting the two vertexes Pns and P2 maycorrespond to the minimum edge EGmin. FIG. 13B represents anintermediate stage of removing the minimum edge Egmin. FIG. 13Cillustrates a structure after the minimum edge EGmin is removed.

The minimum edge EGmin may be removed by combining both vertexes Pns andPs of the minimum edge EGmin. As illustrated in FIG. 13B, the vertex Pnsclassified as the noise component may be changed to the vertex P2′,which is illustrated as distinct from the vertex P2 for convenience ofillustration but the vertex P2′ is identical to the vertex P2. The edgesP1˜P2′ and P2′˜P3 are overlapped with the edges P1˜P2 and P2˜P3. Thus,the edges P1˜P2′ and P2′˜P3 may be deleted from the data underprocessing. As a result, after the minimum edge EGmin is removed, thestructure of FIG. 13A may be modified to the structure of FIG. 13C.

Even though the removal of the data of the structure elements isdescribed with reference to FIGS. 12 through 13C, example embodiments ofthe present disclosure are not limited thereto.

In some example embodiments, a threshold length of the edges may bedetermined and noise edges having a length shorter than the thresholdlength among the edges may be removed. In this case, whenever thesmoothing sub-routine is repeated, two or more edges may be removed orno edge may be removed.

In other example embodiments, whenever the smoothing sub-routine isrepeated, N shortest edges among the entire edges may be removed, whereN is a natural number greater than 1. In this case, both vertexes ofeach noise edge may be combined as described with reference to FIGS.13A, 13B and 13C.

As such, in comparison with conventional schemes of reducing noise byadding structure elements, the method and device according to exampleembodiments may reduce the structure noise with a decreased dataprocessing time by sequentially removing data of structure elements tosimultaneously remove the errors in the input structure.

FIGS. 14A and 14B are diagrams illustrating an example result of a noisereduction according to the method of FIG. 11.

The method of FIG. 11 may be applied even when the signal component isnot detected during boundary condition setting and all structureelements are considered as the noise components. FIG. 14A illustrates anexample input structure STR31 the entire structure elements of which aredetermined as the noise components. FIG. 14B illustrates an outputstructure STR32 corresponding to a result of performing the smoothingoperation with respect to the input structure STR31 according to themethod of FIG. 11. Enlarged view of portions PRT31 and PRT 32 of thestructures STR31 and STR32 are illustrated together in FIGS. 14A and14B.

Structural errors may include mesh intersection such that meshes arecrossed. The structural errors may be caused if the edge is removedwithout the self-structure analysis described above and generallyherein.

The number of the faces of the input structure STR31 is 42048 and thenumber of the faces of the output structure STR32 is 7984. As such thestructure noise may be removed stably by performing the smoothingoperation accompanying the removal of data of a portion of the structureelements and the processing time may be reduced from the conventionalcase of about 180 hours to about 1.5 hour. The operation of smoothingshapes that has been impossible by the conventional methods may beimplemented through the methods described in example embodiments herein.

FIG. 15 is a flow chart illustrating a method of reducing structurenoise according to example embodiments of the present disclosure.

Referring to FIG. 15, input data representing an input structure isprovided (S200). Boundary conditions are set (S440) by classifying dataof each of multiple structure elements of the input data as a signalcomponent or a noise component. A smoothing operation is performed withrespect to the input data based on the boundary conditions (S600). Aportion of the boundary conditions is canceled while the smoothingoperation is performed (S740).

The processes S200, S440, S600 and S800 of the method of FIG. 15 are thesame as those of FIG. 11. Thus, the repeated descriptions are omitted.Hereinafter example embodiments of performing a smoothing operationaccompanying the boundary condition canceling will be described withreference to FIGS. 16 through 18C.

FIG. 16 is a flow chart illustrating an example embodiment of performinga smoothing operation and canceling a portion of boundary conditionsincluded in the method of FIG. 15.

Referring to FIGS. 3 and 16, the smoothing unit 24 performs a smoothingsub-routine (S610). The above mentioned smoothing operation may beperformed such that the smoothing sub-routine is repeatedly executedbased on data representing a structure and boundary conditions.

The control unit 22 may determine whether smoothing is completed (S620)whenever the smoothing sub-routine is finished. The completion ofsmoothing may be determined variously, for example, depending on thescheme of the smoothing process, the degree of the noise reduction, etc.

When the control unit 22 determines that smoothing is completed (S620:YES), the smoothing operation is completed and the smoothing unit 24 mayprovide the output data Dout representing the output structure byreducing noise from the input structure.

When the control unit 22 determines that smoothing is not completed(S620: NO), the control unit 22 cancels a portion of the boundaryconditions (S740) and the smoothing unit 24 performs the smoothingsub-routine again (S610) based on the data and the boundary conditionsexcluding the cancelled portion of the boundary conditions.

FIG. 17 is a diagram for describing an example embodiment of canceling aboundary condition in FIG. 16.

The left portion of FIG. 17 illustrates a vertex Psg classified as asignal component and corresponding vertex faces with normal vectorsrepresented by arrows. The right portion of FIG. 17 illustrates aprocess of canceling a boundary condition based on a comparison resultof a vertex face angle θ and a threshold angle θ Th.

Referring to FIG. 17, the vertex face angle θ with respect to the vertexPsg classified as the signal component may be calculated and thethreshold angle θ_(Th) of the vertex face angles may be determined. Theboundary condition may be canceled based on comparison results of thevertex face angle θ and the threshold angle θ_(Th). Here the vertex Psgclassified as the signal component indicates a vertex to which theboundary condition is set such that the movement of the vertex Psg isrestricted during the smoothing operation as described above.

When the vertex face angle θ is smaller than the threshold angle θ_(Th),the vertex Psg may maintain the boundary condition. Thus, the movementof the vertex Psg may be restricted during the smoothing operation. Incontrast, when the vertex face angle θ is smaller than the thresholdangle θ_(Th), the signal vertex Psg may be changed to the noise vertexPns, that is, the boundary condition of the vertex Psg may be canceled.Thus, the vertex Pns may move freely during the smoothing operation.

FIGS. 18A, 18B and 14C are diagrams illustrating an example result of anoise reduction according to the method of FIG. 15.

FIG. 18A illustrates an example input structure STR41. FIG. 18Billustrates an output structure STR42 corresponding to a result ofperforming the smoothing operation with respect to the input structureSTR41 without cancelling of the boundary conditions. FIG. 18Cillustrates an output structure STR43 corresponding to a result ofperforming the smoothing operation with respect to the input structureSTR41 with cancelling of the boundary conditions.

In a complex structure, a shape may be distorted because the boundaryconditions may be set not by the shape itself but by the adjacent shape.Due to a boundary condition set to a vertex, a spike may be caused andthe spike may block the removal of the noise component. Such spikes maybe removed efficiently by performing the smoothing operationaccompanying the cancel of the boundary conditions.

As illustrated in FIG. 18B, spikes corresponding to the vertexesPs1˜Ps10 may be caused if the smoothing operation is performed withoutcancelling the boundary condition. In contrast, as illustrated in FIG.18C, the spikes may be removed if the smoothing operation is performedwith cancelling of the boundary condition according to exampleembodiments.

FIG. 19 is a flow chart illustrating an example embodiment of performinga smoothing operation, removing data of a portion of structure elementsand canceling a portion of boundary conditions included in a method ofreducing structure noise according to example embodiments.

Referring to FIGS. 3 and 19, the smoothing unit 24 performs a smoothingsub-routine (S610). The above mentioned smoothing operation may beperformed such that the smoothing sub-routine is repeatedly executedbased on data representing a structure and boundary conditions.

The control unit 22 may determine whether smoothing is completed (S620)whenever the smoothing sub-routine is finished. The completion ofsmoothing may be determined variously, for example, depending on thescheme of the smoothing process, the degree of the noise reduction, etc.

When the control unit 22 determines that smoothing is completed (S620:YES), the smoothing operation is completed and the smoothing unit 24 mayprovide the output data Dout representing the output structure byreducing noise from the input structure.

When the control unit 22 determines that smoothing is not completed(S620: NO), the control unit 22 removes data of a portion of structureelements (S720) as described with reference to FIGS. 11 through 14B andcancels a portion of the boundary conditions (S740) as described withreference to FIGS. 15 through 18C. The smoothing unit 24 performs thesmoothing sub-routine again (S610) based on the data and the boundaryconditions excluding the removed portion of the structure elements andthe cancelled portion of the boundary conditions.

FIGS. 20A, 20B and 20C are diagrams for describing an example embodimentof a smoothing operation included in a method of reducing structurenoise according to example embodiments of the present disclosure.

In some example embodiments, a smoothing operation may be performed withrespect to a noise vertex P classified as a noise component based onarea information of a three-dimensional mesh corresponding to the noisevertex P and area information of a two-dimensional mesh corresponding toa projection of the noise vertex P to a plane along a direction of anormal vector. The smoothing operation may be performed using a firstarea An(s) of the three-dimensional meshes adjacent to the noise vertexP and a second area A′n(S) of the two-dimensional meshes.

As illustrated in FIG. 20A, the noise vertex P has the adjacent meshes.The normal vector m(T) of the noise vertex P may be obtained using thenormal vectors n(T) of the adjacent meshes as Equation4.

$\begin{matrix}{{m(T)} = \frac{\sum\limits_{i = 1}^{n}{n(T)}}{n}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

In Equation4, n indicates a total number of the adjacent meshes and n(T)indicates the normal vector of each adjacent mesh.

The noise vertex P in three dimensions of the left portion of FIG. 20Bmay be projected along the plane perpendicular to the normal vector m(T)as illustrated in the right portion of FIG. 20B.

The first area An(s) of the three-dimensional meshes may be obtained byEquation5.

$\begin{matrix}{{A_{n}(s)} = {\sum\limits_{i = 1}^{n}{A_{n}(T)}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

In Equation5, T indicates the mesh adjacent to the noise vertex P andAn(T) indicates an area of each mesh.

The second area A′n(S) of the two-dimensional meshes may be obtained,for example, by a Gauss-Green method.

The area ratio An(s)/A′n(s) may be obtained from the first area An(s) ofthe three-dimensional meshes and the second area A′n(S) of thetwo-dimensional meshes. An average

of the area ratios An(s)/A′n(s) of all noise vertexes may be obtained byEquation6.

$\begin{matrix}{\overset{\_}{m} = \frac{\sum\limits_{i = 1}^{n}\left\lbrack \frac{A_{n}(s)}{A_{n}^{\prime}(s)} \right\rbrack}{n}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

In Equation6, n indicates a total number of the noise vertexesrepresenting a three-dimensional structure.

A displacement of the noise vertex P may be obtained using the firstarea An(s) of the three-dimensional meshes, the second area A′n(S) ofthe two-dimensional meshes, the average

and the standard deviation a of the area ratios An(s)/A′n(s), asEquation7.D=min(A(s)−A′(s)

+σ))  Equation7

In other words, the displacement D of the noise vertex P may be obtainedby finding a minimum difference between the first area An(s) of thethree-dimensional meshes and the corrected value of the second areaA′n(S) of the two-dimensional meshes, as adjusted by the average andstandard deviation of the area ratios An(s)/A′n(s). In the left portionof FIG. 22C, the area of the polygon at the bottom plane corresponds tothe second area A′n(S) of the two-dimensional meshes, and the area ofthe six triangular meshes corresponds to the first area An(s) of thethree-dimensional meshes. The smoothing operation may be performed suchthat the displacement D may be applied to update the noise vertex P.

The left portion of FIG. 20C illustrates an old position Pold of thenoise vertex P and the right portion of FIG. 20C illustrates a newposition Pnew of the noise vertex P when the displacement D is applied.

Even though the smoothing operation has been described with reference toFIGS. 20A, 20B and 20C, example embodiments are not limited thereto. Thevarious smoothing schemes may be adopted as will be appreciated by oneskilled in the art.

FIG. 21 is a flow chart illustrating a method of reducing structurenoise according to example embodiments of the present disclosure.

Referring to FIG. 21, input data representing an input structure isprovided (S200). The input structure may be a two-dimensional structureor a three-dimensional structure and the input structure may be providedthrough various methods. In some example embodiments, the input data maybe generated by an electronic design automation (EDA) tool. In otherexample embodiments, the input data may be provided by processing imagedata that is captured using an image sensor. In still other exampleembodiments, the input data may be provided by restructuring data usinga computer vision. The method of providing the input data is not limitedthereto and the input data may be provided through other variousmethods.

Boundary conditions are set (S440). In some example embodiments, theboundary conditions may be set by classifying data of each of multiplestructure elements of the input data as a signal component or a noisecomponent. In other example embodiments, the boundary conditions may beaccepted from a user. In still other example embodiments, the boundaryconditions may be set by classifying the structure elements with thesettings from the user.

A smoothing operation is performed with respect to the input data basedon the boundary conditions (S600). A portion of the structure elementsis combined after the smoothing operation is completed (S760). After thesmoothing operation and the combining of the structure elements, outputdata representing an output structure is provided by reducing noise fromthe input structure (S800).

The processes S200, S440, S600 and S800 of the method of FIG. 21 are thesame as those of FIG. 11. Thus, the repeated descriptions are omitted.Example embodiments of combining the structure elements will bedescribed below with reference to FIGS. 22 through 24B.

FIG. 22 is a flow chart illustrating an example embodiment of combiningstructure elements included in the method of FIG. 21.

FIG. 22 illustrates an example of removing an edge for combining thestructure elements. Referring to FIG. 22, data representing a structureis provided (S771). The data may be the above-mentioned input data orthe data after the smoothing operation is completed.

With respect to each edge, an edge angle θ_(E) is calculated and athreshold angle θ_(ETh) of the edge angles may be determined. Theadjacent faces may be combined based on comparison results of the edgeangle θ_(E) and the threshold angle θ_(ETh).

When the edge angle θ_(E) is greater than the threshold angle θ_(ETh)(S772: YES), the corresponding edge may be maintained (S773). When theedge angle θ_(E) is smaller than the threshold angle θ_(ETh) (S772: NO),the corresponding edge may be removed and the adjacent edges may berestructured (S774).

When the process is completed for all edge angles (S775: YES), the datarepresenting a final structure is provided (S776). When the process isnot completed for all edge angles (S775: YES), the above processes S772,S773 and S774 are repeated for another edge angle.

FIGS. 23A and 23B are diagrams for describing an example embodiment ofcombining structure elements of FIG. 22.

FIG. 23A illustrates a first structure STRa, a second structure STRb, athird structure STRc and enlarged views of a first portion PRTa of thefirst structure STRa and a second portion PRTb of the second structureSTRb. FIG. 23B illustrates an edge angle θ_(E) corresponding to an edgeEGk.

As illustrated in FIG. 23B, the edge angle θ_(E) may be obtained usingthe normal vectors N(T) of the faces adjacent to the edge EGk connectingthe two vertexes Pk1 and Pk2. It is assumed that the edge angle θ_(E)satisfies the condition(s) of FIG. 22 for removing the edge EGk.

Referring to FIGS. 23A and 23B, the two vertexes Pk1 and Pk2 of the edgeEGk in the first structure STRa may be combined into the one edge Pk inthe second structure STRb. Accordingly the edge EGk may be removed andthe adjacent edges may be restructured. After such removal of the edgesis completed, the first structure STRa may be modified to the thirdstructure STRc simpler than the first structure STRa.

FIGS. 24A and 24B are diagrams illustrating an example result of a noisereduction according to the method of FIG. 21.

To reduce the data processing time and enhance probability reducing thenoise, it is required to decrease the number of the structure elementswhile the shape of the structure is maintained. For example, each edgeangle θ_(E) may be calculated and each edge angle θ_(E) may be comparedwith the threshold angle θ_(ETh). If the edge angle θ_(E) is greaterthan the threshold angle θ_(ETh), the corresponding edge may beconsidered as the signal component defining the structure. If the edgeangle θE is smaller than the threshold angle θ_(ETh), the correspondingedge may be removed as the noise component and the adjacent faces may becombined.

FIG. 24A illustrates an example structure STR51 before combining thestructure elements. FIG. 24B illustrates a structure STR52 correspondingto a result of combining the structure elements with respect to thestructure STR51 as described above. The structure STR52 of FIG. 24Bincludes 2762 vertexes whereas the structure STR51 of FIG. 24A includes8082 vertexes. As such the complex structure STR51 may be simplified tothe structure STR52 with 34% of the original vertexes.

FIG. 25 is a block diagram illustrating an electronic system accordingto example embodiments of the present disclosure.

Referring to FIG. 25, a computer-based electronic system 1000 mayinclude a memory device MEM 1100, a processor CPU 200, a read onlymemory device ROM 300, a storage device 400, an input-output device I/O500, a communication interface COMM I/F 600, and a signal bus 700electrically connecting them.

The memory device 1100 may, as a main memory, store operation system OS1010, a noise reduction program NRP 1020 for reducing structure noise,and data 1030.

The method of and device for reducing structure noise according toexample embodiments may be implemented with hardware, software or acombination of hardware and software. For example, the device forreducing structure noise as described with reference to FIG. 2 may beimplemented using a program stored in the memory device 1100 that isreadable by the processor 200.

The input device in the input-output device 500 may receive input datarepresenting an input structure. The memory device may store informationincluding program routines for setting boundary conditions byclassifying data of each of multiple structure elements of the inputdata as a signal component or a noise component, performing a smoothingoperation with respect to the input data based on the boundaryconditions and providing output data representing an output structure byreducing noise from the input structure. The output device included inthe input-output device 500 may display the input structure and theoutput structure. The processor 200 may be connected to the inputdevice, the output device and the memory device to control an executionof the program routines.

As described above, the method and device according to exampleembodiments of the present disclosure may reduce structure noiseefficiently with respect to an arbitrary input structure withoutrequiring additional information on the signal components of the inputstructure by analyzing the input structure itself to set the boundarycondition and by separating the noise from the data of the validstructure. In addition, in comparison with conventional schemes ofreducing noise by adding structure elements, the method and deviceaccording to example embodiments of the present disclosure may reducethe structure noise with a decreased data processing time bysequentially removing data of structure elements to simultaneouslyremove the errors in the data of the input structure.

The inventive concepts of the present disclosure may be applied tovarious fields requiring analysis and/or processing of a two dimensionalor three-dimensional structure. For example, the inventive concepts maybe applied to fields for analyzing and processing data generated by anelectronic design automation (EDA) tool, data provided by processingimage data that is captured using an image sensor, data provided byrestructuring data using a computer vision.

The foregoing is illustrative of example embodiments and is not to beconstrued as limiting thereof. Although a few example embodiments havebeen described, those skilled in the art will readily appreciate thatmany modifications are possible in the example embodiments withoutmaterially departing from the concepts described herein.

What is claimed is:
 1. A method of reducing structure noise, comprising:obtaining input data representing an input structure; setting boundaryconditions defining boundaries of structure elements of the inputstructure by classifying data of each of multiple structure elements ofthe input data as a signal component or a noise component; performing asmoothing operation with respect to the input data based on the boundaryconditions; and providing output data representing an output structureby reducing noise from the input structure.
 2. The method of claim 1,wherein setting the boundary conditions is performed by analyzing onlythe input data without additional information on the input structure. 3.The method of claim 1, wherein setting the boundary conditions includes:analyzing plane equations of faces included in the structure elements;grouping the faces into face groups based on the plane equations of thefaces; calculating area ratios, each area ratio corresponding to a ratioof a total area of each face group with respect to an entire area of thefaces; determining a threshold ratio of each area ratio; and setting theboundary conditions based on comparison results of the area ratios andthe threshold ratio.
 4. The method of claim 3, wherein setting theboundary conditions based on the comparison results includes:classifying the faces of a face group having an area ratio greater thana threshold ratio as the signal component; and classifying the faces ofa face group having an area ratio smaller than a threshold ratio as thenoise component.
 5. The method of claim 4, wherein setting the boundaryconditions based on the comparison results further includes: classifyinga first vertex as the signal component if the first vertex pertains toat least one of the faces that are classified as the signal component;classifying a second vertex as the noise component if the second vertexpertains to only the faces that are classified as the noise component;and setting the boundary conditions with respect to vertexes that areclassified as the signal component.
 6. The method of claim 3, whereindetermining the threshold ratio of each area ratio includes: determiningthe threshold ratio based on a distribution of numbers of the facegroups.
 7. The method of claim 6, wherein a range of the area ratios isobtained such that the numbers of the face groups in the range are zerocontinuously and a center value of the range of the area ratios isdetermined as the threshold ratio.
 8. The method of claim 6, wherein thethreshold ratio is determined based on the distribution of numbers ofthe face groups.
 9. The method of claim 1, further comprising: removingdata of a portion of the structure elements while the smoothingoperation is performed.
 10. The method of claim 9, wherein removing thedata of the portion of the structure elements includes: removing aminimum edge having a shortest length among edges included in thestructure elements.
 11. The method of claim 10, wherein removing theminimum edge includes: combining each vertex of the minimum edge. 12.The method of claim 9, wherein the removing the data of the portion ofthe structure elements includes: determining a threshold length of edgesincluded in the structure elements; and removing noise edges having alength shorter than the threshold length among the edges.
 13. The methodof claim 1, further comprising: canceling a portion of the boundaryconditions while the smoothing operation is performed.
 14. The method ofclaim 13, wherein canceling the portion of the boundary conditionsincludes: calculating vertex face angles with respect to vertexesclassified as the signal component among vertexes included in thestructure elements; determining a threshold angle of the vertex faceangles; and canceling the boundary conditions based on comparisonresults of the vertex face angles and the threshold angle.
 15. Themethod of claim 1, further comprising: combining a portion of thestructure elements after the smoothing operation is completed.
 16. Themethod of claim 15, wherein combining the portion of the structureelements includes: calculating edge angles with respect to edgesincluded in the structure elements; determining a threshold angle of theedge angles; and combining adjacent faces based on comparison results ofthe edge angles and the threshold angle.
 17. The method of claim 1,further comprising: generating and displaying a representation of theoutput structure based on the output data.
 18. The method of claim 17,wherein the representation of the output structure has fewer structureelements than the representation of the input structure as a result ofreducing the noise.
 19. A device for reducing structure noise,comprising: a controller configured to set boundary conditions definingboundaries of structure elements of the input structure by classifyingdata of each of multiple structure elements of input data as a signalcomponent or a noise component based on input data representing an inputstructure; and a smoothing component configured to perform a smoothingoperation with respect to the input data based on the boundaryconditions and to provide output data representing an output structureby reducing noise from the input structure.
 20. A computer-basedelectronic system for reducing structure noise, comprising: an inputdevice configured to receive input data representing an input structure;a memory device configured to store information including programroutines that, when executed, set boundary conditions definingboundaries of structure elements of the input structure by classifyingdata of each of multiple structure elements of the input data as asignal component or a noise component, perform a smoothing operationwith respect to the input data based on the boundary conditions, andprovide output data representing an output structure by reducing noisefrom the input structure; an output device configured to display theinput structure and the output structure; and a processor connected tothe input device, the output device and the memory device, the processorconfigured to control execution of the program routines.